We propose to utilize the Multiethnic Cohort (MEC), a PAGE Phase 1 study, to sample and assess genomic variation in well-phenotyped individuals of non-European (EA) ancestry. The MEC is a prospective population-based cohort of over 215,000 men and women in Hawaii and California that includes large representations of older adults for five racial/ethnic groups (African Americans, Latinos, Japanese Americans, Native Hawaiians and European Americans) at varying risks of chronic diseases. In the MEC, we have established a biorepository of blood and urine samples (N>74,000) linked to prospectively collected risk factor, biomarker and clinical/endpoint data. In PAGE2, we propose to explore the associations of DNA sequence variation for a broad range of phenotypes, focusing on conditions with disproportionate disease burdens in non-EAs. We propose to utilize the most comprehensive and cost-effective SNP genotyping array to capture and perform association tests in non-EA populations for: a) common and rare putative functional coding variation, and; b) predicted functional variants in ENCODE-defined regulatory elements in non-coding sequence at GWAS-identified risk loci. We will focus on diseases/traits/biomarkers that are common to PAGE2 studies and for which we have DNA available for large numbers of MEC subjects (e.g., type 2 diabetes, obesity, common cancers, fasting insulin, glucose, lipids, etc.). We will analyze single variants, risk scores, and burden of rare variants for single genes and pathways using both directional and omnibus methods. Using the comprehensive risk factor information available across PAGE2, we will also examine interactions between gene (e.g., SNP, risk score, burden), pathway and environment and build risk prediction models for the purpose of better defining the genetic contributions to racial/ethnic disparities in risk for common chronic diseases and traits. Last, we also propose to develop novel statistical methods and approaches for analyzing rare variants including: (a) an examination of kernel machine tests in the presence of subtle hidden population stratification; (b) extending the kernel machine methods to form tests of gene/pathway x environment and gene/pathway x gene/pathway interactions, and; (c) how to apply these methods to further characterize whether variants in specific pathways and their interactions explain racial/ethnic differences in risk. Working with other PAGE2 studies, the Coordinating Center and NHGRI, we hope to form a valuable population-based resource for the scientific community that will expand our understanding of ancestral differences in genomic disease associations and the role of genetic variation in understanding racial and ethnic differences in disease risk.